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Updated: May 18, 2026

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images
Published on: September 22, 2023
Vessel specific coronary artery calcium scoring: an automatic system
Rahil Shahzad1, Theo van Walsum, Michiel Schaap
1Quantitative Imaging Group, Delft University of Technology, Delft, The Netherlands. r.shahzad@tudelft.nl
Insights
An automated system accurately detects and quantifies coronary artery calcium, improving risk assessment for cardiovascular disease. This tool shows high correlation with manual scoring for whole heart and individual arteries.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Cardiology
Background:
- Coronary artery calcification (CAC) is a key indicator of coronary artery disease.
- Accurate quantification of CAC is crucial for cardiovascular risk stratification.
- Manual calcium scoring from cardiac computed tomography (CT) images can be time-consuming and subject to interobserver variability.
Purpose of the Study:
- To develop and validate an automated method for detecting and quantifying calcium lesions.
- To assess the system's performance for the whole heart and individual coronary arteries.
- To evaluate the system's accuracy in risk category classification compared to manual scoring.
Main Methods:
- Utilized non-contrast-enhanced cardiac CT images from 366 patients.
- Trained an automatic detection and quantification system on 209 datasets and tested on 157.
- Compared automated results against manual observer scores (ground truth) using Pearson's correlation and Bland-Altman analysis.
Main Results:
- Achieved high sensitivity for calcified object detection (81.2% for 1.5-mm, 86.6% for 3.0-mm slices).
- Demonstrated strong correlation (R=0.97) for whole heart calcium volume scores.
- Showed high accuracy in risk category assignment (95% for 1.5-mm, 89% for 3.0-mm scans).
Conclusions:
- An automated, vessel-specific coronary artery calcium scoring system was successfully developed.
- The system demonstrates feasibility for accurate calcium scoring in individual vessels.
- The automated system shows potential for reliable cardiovascular risk stratification.
Rationale And Objectives:
The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images.
Materials And Methods:
Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearson's correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxon's signed-rank test was used to compare the interobserver variability to the system's performance.
Results:
Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearson's correlation coefficients of 0.97 (P < .001) for both 1.5-mm and 3.0-mm scans with respect to the calcium volume score of the whole heart were found. The average R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans.
Conclusions:
An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated.
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